Before digging into this post, I recommend reading Part One of this series if you haven’t already. In Part One, I explain my process of web scraping data from Basketball-Reference for the 2020–2021 Washington Wizards NBA team and restructuring it into a manipulable dataframe in Python. Posted below is the code and output I ended with:

The 2020–2021 NBA season is finally upon us! To celebrate this momentous occasion as a budding data scientist and long-time NBA fan, I thought it would be a fun practice to web scrape data from Basketball-Reference, a site that holds statistics for all things professional basketball, from the NBA to the WNBA and G League. Since I support the Washington Wizards (yes, it’s tough out here so far, but we have to stay loyal 😤), the focus for this post will be to extract per game 2020–2021 statistics for each Wizards player, as well as some other individual information.


Friends and family expressed surprise when hearing of my recent decision to enroll in a data science coding boot camp. During high school and college, I had gravitated toward literature and the humanities, and my parents pictured me practicing law or researching legislative policy, as opposed to being tucked away behind big-screen monitors writing statistical coding programs. So now, everyone’s asking me, “Why did you leave your good job to study data science?” …

Gabriel Cano

I'm a recent graduate of General Assembly's Data Science Immersive program, passionate to grow every day, and excited to share my journey as a data scientist!

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